The invention relates generally to medical data processing and display, and more particularly, to a system and method for collecting, analyzing, and displaying analyses of medical analyte data for managing diabetes mellitus.
Diabetes mellitus, or simply, “diabetes,” is an incurable chronic disease. Type 1 diabetics must manage their diabetes by taking a glucose-lowering medication, such as insulin, to compensate for the rise in blood glucose that follows food consumption. Type 1 diabetes management works to prevent hyperglycemia, or high blood glucose, while especially averting the consequences of hypoglycemia, or low blood glucose, from over-aggressive or incorrect insulin dosing. Poor diabetes management can manifest in acute symptoms, such as loss of consciousness, or through chronic conditions, including cardiovascular disease, retinopathy, neuropathy, and nephropathy. Effective diabetes management requires effort.
Many different ways exist to assist in monitoring and managing one's glucose levels. Health care maintenance systems based on the use of a handheld device are often used. These devices are configured to record patient data, such as blood glucose data. Additionally, it is known that such data can be uploaded to a remote server for storage of large quantities of medical data and later access to it by third parties, such as the patient's health care providers (“HCP”). Examples are Google Health and Microsoft HealthVault™. At the remote server location or elsewhere, blood glucose test results can be matched with quantitative information on medication, meals, or other factors, such as exercise.
Medical sensors can generate large quantities of useful information about a physiological parameter or parameters of a patient. That information, when processed, organized, and analyzed in particular ways, can be highly beneficial to an HCP in examining the patient and recommending treatment. The appropriate calculations, organization, and analyses of that data can assist in forming rapid, useful, and more accurate evaluations of the information, the patient's history, and the patient's present state and health condition.
For example, analyte monitoring and medication delivery devices are commonly used in the treatment of a patient. One or more samples of analytes from the patient's body tissues are sensed and data is accumulated. A monitor, containing a sensor and a processor, may be used to acquire, accumulate, and process that data. Ultimately a report must be produced from that data and an analysis made by an HCP. In response to the analysis, one or more medications may be administered to the patient or other course of treatment prescribed, such as exercise and control over the timing, amount, and contents of meals. Administration of the medication may be manual by the patient such as self-injection with a syringe, by another person such as a nurse, or by a powered medication administration device, such as an infusion pump, for automatic or continuous delivery. For example, glucose monitors and insulin pumps are commonly used in the treatment and management of type 1 diabetes mellitus.
In the case of diabetes, a blood glucose monitor (“BGM”) or continuous glucose monitor (“CGM”) may be used in obtaining data about the glucose level of a patient. Such sensors detect glucose levels through actual analysis of a drop of blood, or through sensing the composition of interstitial tissue. The patient may have a handheld digital device, such as a personal digital assistant (“PDA”) that is used to receive and store his or her glucose data. This can occur in a number of ways. In the case where the patient draws a drop of blood onto a test strip that is read by a BGM, the data from the BGM may be communicated to the PDA for storage, processing (such as by adding a date and time stamp), and transfer elsewhere.
In one case, the BGM is integrated with the PDA (dedicated device) and in another case, both the BGM and the PDA may be integrated into a mobile telephone with the appropriate hardware and software as a single unit. In another case, the glucose data is communicated to the PDA wirelessly or through a wired connection. In both cases of the BGM and CGM, various schemes may be used to get measured patient glucose data onto the PDA. The PDA is programmed to process that data and can provide a useful number representation of a glucose level on the screen of the PDA, and can also be instructed to upload the data to a server that may be remote and which may be accessed through the Internet (cloud computing) or by other means. Conveniently, a computerized report can be used to display such measurements and calculations of the measured glucose together and can be analyzed for use in developing health management recommendations. For example, glucose monitors are programmed to provide recommendations for better blood glucose management in the patient. Such analyses often include trends, extrapolations, predictions, alerts, and others.
Accordingly, the detection of the level of analytes, such as glucose, lactate, oxygen, and the like, in certain individuals is vitally important to their health. Moreover, analyzing these analytes and recording analytics relating thereto, as well as other patient behavior, such as activities and meals, and providing this information to HCPs for analysis can provide valuable, life-saving feedback to patients who have difficult medical conditions. For example, monitoring glucose levels is particularly important to individuals with diabetes as well as monitoring diet and exercise, to determine when insulin is needed to reduce glucose levels in their bodies or when additional glucose is needed to raise the level of glucose in their bodies. The provision of related analytics of their glucose levels to an HCP may result in a therapy recommendation that may be useful in helping the patient better manage his or her diabetes. Existing data management and analysis tools are available and are further being developed to assist patients along these lines.
Previous glycemic control risks have been assessed visually by trained experts who have developed skills in balancing the competing demands of consistently lowering glucose levels while avoiding excessive hypoglycemia. Typically these experts review plots or tables of glucose values. These skills are hard to acquire and transfer to others.
Self-monitoring blood glucose (“SMBG”) testing schedules are assigned to patients by HCPs in order to gather data so that the HCPs can make recommendations to patients regarding therapy and lifestyle changes. Key metrics that can be ascertained by this SMBG testing are median glucose, low range variability, and hypoglycemia risk. Typically a key therapy goal is to reduce a patient's median glucose while avoiding the risk of the patient spending significant time in hypoglycemia or experiencing a severe hypoglycemia incidence. The higher a patient's low range variability, the higher the median glucose the patient will need to maintain in order to avoid these incidences of hypoglycemia.
Some of the problems with SMBG testing schedules are patient compliance and limited data. Patients may not comply with an SMBG testing schedule because blood glucose (“BG”) testing can be painful and inconvenient. In order to maximize compliance, SMBG test schedules generally occur over a short time period with just a handful of SMBG tests. This leads to the second problem, limited data. SMBG testing schedules will produce relatively small data sets which can introduce a high uncertainty to the calculated median glucose, calculated low range variability, and calculated hypoglycemia risk. The higher the uncertainty, the less aggressive the treatment recommendations can be in order to be sure that the hypoglycemia risks are avoided.
Additionally, another problem caused by collecting a small amount of data is that SMBG measurements can either be focused on a small number of short time periods or long time periods, but not both. For example, an SMBG test schedule might focus on median and variability at fixed times, for example one hour after meals, requiring the patient to perform tests every day for one to two weeks one hour after each scheduled meal. With such a test schedule, the median and low range variability can be calculated relatively accurately, but only for one hour after each scheduled meal. Little information will be learned about other time periods (such as two hours after each meal). Alternatively, the SMBG test schedule may follow a progressive schedule requiring the patient to test at various times of the day. For example the schedule might ask for the patient to test at 7:00 AM, 11:00 AM, 3:00 PM, and 7:00 PM one day, and then 8:00 AM, 12:00 PM, 4:00 PM, and 8:00 PM the next day for one to two weeks. This type of SMBG test schedule can produce a relatively accurate portrayal of median and low range variability during the entire range of times tested. It is unlikely that a patient will comply with a testing schedule that requires a test during sleeping hours day after day.
Continuous glucose monitors (“CGMs”) are also given to patients by HCPs to measure a patient's median glucose, low range variability, and hypoglycemia risk. By using a CGM, most of the problems associated with discrete blood glucose testing with BGMs can be addressed. With a CGM, one typically doesn't need to worry about patient compliance. There is enough data to measure low range variability to very small time periods, typically as short as one hour. Additionally, CGM systems provide data while the patient is sleeping.
The drawbacks of CGM are that it is relatively expensive, it can be uncomfortable, and patients must typically wear a device continuously, day and night, which many are very reluctant to do. It would therefore be helpful if a patient were able to wear a CGM for shorter periods of time, yet still obtain enough useful data to more accurately monitor and manage blood glucose.
Hence, those skilled in the art have recognized that there is a need for a system and a method that more accurately determine blood glucose levels in a patient. Another recognized need is for requiring the more useful and efficient collection of blood glucose data from patients so that patients will have a higher compliance level with a testing schedule. Another need is for an analysis system and method of the blood glucose data of a patient to consider variation in blood glucose levels so that glycemic risk can be determined and better treatment can result. A further need is for a clearer analysis and display of glucose data so that treatment can be prescribed with a small risk that varying blood glucose levels may cause hypoglycemic incidence. The present invention fulfills these needs and others.